Unsupervised Learning - Clustering and Association Algorithms in Machine Learning

Machine Learning is one of the most effectively used technology. Currently, all the companies are using this technology as they can use their data to understand the important areas from where they can grow their business. Understanding all of this, we have come up with this tutorial on "Unsupervised Learning" which is a very important clustering algorithm.

As we all know that Supervised machine learning is a method in which models are trained using labeled data under the supervision of training data. But there may be many cases in which we do not have labeled data and need to find the hidden patterns from the given dataset. So, to solve such types of cases in machine learning, we need unsupervised learning techniques. Unsupervised learning cannot be directly applied to a regression or classification problem because unlike supervised learning, we have the input data but no corresponding output data.

Unsupervised learning is a machine learning technique in which models are not supervised using a training dataset. Instead, the models itself find the hidden patterns and insights from the given data. It can be compared to learning which takes place in the human brain while learning new things. The goal of unsupervised learning is to find the underlying structure of the dataset, group that data according to similarities, and represent that dataset in a compressed format. In this tutorial, we will help you to cover every concept related to Unsupervised Learning.

Introduction.
Introduction To Unsupervised Learning Algorithm.
Euclidean and Non Euclidean Distances.
K Means Clustering.
Demo.